Evaluating and Explaining Climate Science

Archive for July, 2015

I wanted to take a look at Renewable Energy because it’s interesting and related to climate science in an obvious way. Information from media sources confirms my belief that 99% of what is produced by the media is rehashed press releases from various organizations with very little fact checking. (Just a note for citizens alarmed by this statement – they are still the “go to source” for the weather, footage of disasters and partly-made-up stories about celebrities).

Regular readers of this blog know that the articles and discussion so far have only been about the science – what can be proven, what evidence exists, and so on. Questions about motives, about “things people might have done”, and so on, are not of interest in the climate discussion (not for this blog). There are much better blogs for that – with much larger readerships.

Opinions
Opinions are often interesting and sometimes entertaining. But what do we learn from opinions? It’s more useful to understand the science behind the subject. What is this particular theory built on? How long has the theory been “established”? What lines of evidence support this theory? What evidence would falsify this theory? What do opposing theories say?Anything else?
This blog will try and stay away from guessing motives and insulting people because of how they vote or their religious beliefs. However, this doesn’t mean we won’t use satire now and again as it can make the day more interesting.

The same principles will apply for this discussion about renewables. Our focus will be on technical and commercial aspects of renewable energy, with a focus on evidence rather than figuring it out from “motive attribution”. And wishful thinking – wonderful though it is for reducing personal stress – will be challenged.

As always, the moderator reserves the right to remove comments that don’t meet these painful requirements.

Here’s a claim about renewables from a recent media article:

By Bloomberg New Energy Finance’s most recent calculations a new wind farm in Australia would cost $74 a megawatt hour..

..”Wind is already the cheapest, and solar PV [photovoltaic panels] will be cheaper than gas in around two years, in 2017. We project that wind will continue to decline in cost, though at a more modest rate than solar. Solar will become the dominant source in the longer term.”

I couldn’t find any evidence in the article that verified the claim. Only that it came from Bloomberg New Energy Finance and was the opposite of a radio shock jock. Generally I favor my dogs’ opinions over opinionated media people (unless it is about the necessity of an infinite supply of Schmackos starting now, right now). But I have a skeptical mindset and not knowing the wonderful people at Bloomberg I have no idea whether their claim is rock-solid accurate data, or “wishful thinking to promote their products so they can make lots of money and retire early”.

Calculating the cost of anything like this is difficult. What is the basis of the cost calculation? I don’t know if the claim in BNEF’s calculation is “accurate” – but without context it is not such a useful number. The fact that BNEF might have some vested interest in a favorable comparison over coal and gas is just something I assume.

But, like with climate science, instead of discussing motives and political stances, we will just try and figure out how the numbers stack up. We won’t be pitting coal companies (=devils or angels depending on your political beliefs) against wind turbine producers (=devils or angels depending on your political beliefs) or against green activists (=devils or angels depending on your political beliefs).

Instead we will look for data – a crazy idea and I completely understand how very unpopular it is. Luckily, I’m sure I can help people struggling with the idea to find better websites on which to comment.

Calculating the Cost

I’ve read the details of a few business plans and I’m sure that most other business plans also have the same issue – change a few parameters (=”assumptions”, often “reasonable assumptions”) and the outlook goes from amazing riches to destitution and bankruptcy.

The cost per MWHr of wind energy will depend on a few factors:

cost of buying a wind turbine

land acquisition/land rental costs

installation cost

grid connection costs

the “backup requirement” aka “capacity credit”

cost of capital

lifetime of equipment

maintenance costs

% utilization (output energy / nameplate capacity)

And of course, in any discussion about “the future”, favorable assumptions can be made about “the next generation”. Is the calculation of $74/MWHr based on what was shipped 5 years ago and its actuals, or what is suggested for a turbine purchased next year?

If you want wind to look better than gas or coal – or the converse – there are enough variables to get the result you want. I’ll be amazed if you can’t change the relative costs by a factor of 5 by playing around with what appear to be reasonable assumptions.

Perhaps the data is easy to obtain. I’m sure many readers have some or all of this data to hand.

Moore’s Law and Other Industries

Most people are familiar with the now legendary statement from the 1960s about semiconductor performance doubling every 18 months. This revolution is amazing. But it’s unusual.

There are a lot of economies of scale from mass production in a factory. But mostly limiting cases are reached pretty quickly, after which cost reductions of a few percent a year are great results – rather than producing the same product for 1% of what it cost just 10 years before. Semiconductors are the exception.

When a product is made from steel alloys, carbon fiber composites or similar materials we can’t expect Moore’s law to kick in. On the other hand, products that rely on a combination of software, electronic components and “traditional materials” and have been produced on small scales up until now can expect major cost reductions from amortizing costs (software, custom chips, tooling, etc) and general economies of scale (purchasing power, standardizing processes, etc).

In some industries, rapid growth actually causes cost increases. If you want an experienced team to provide project management, installation and commissioning services you might find that the boom in renewables is driving those costs up, not down.

A friend of mine working for a natural gas producer in Queensland, Australia recounted the story of the cost of building a dam a few years ago. Long story short, the internal estimates ranged from $2M to $7M, but when the tenders came in from general contractors the prices were $10M to $25M. The reason was a combination of:

escalating contractor costs (due to the boom)

compliance with new government environmental regulations

compliance with the customer’s many policies / OH&S requirements

the contractual risk due to all of the above, along with the significant proliferation of contract terms (i.e., will we get sued, have we taken on liabilities we don’t understand, etc)

The point being that industry insiders – i.e., the customer – with a strong vested interest in understanding current costs was out by a factor of more than three in a traditional enterprise. This kind of inaccuracy is unusual but it can happen when the industry landscape is changing quickly.

Even if you have signed a fixed price contract with an EPC you can only be sure this is the minimum you will be paying.

The only point I’m making is that a lot of costs are unknown even by experienced people in the field. Companies like BNEF might make some assumptions but it’s a low stress exercise when someone else will be paying the actual bills.

Intermittency & Grid Operators

We will discuss this further in future articles. This is a key issue between renewables and fossil fuel / nuclear power stations. The traditional power stations can create energy when it is needed. Wind and solar – mainstays of the renewable revolution – create energy when the sun shines and the wind blows.

As a starting point for any discussion let’s assume that storing energy is massively uneconomic. While new developments might be available “around the corner”, storing energy is very expensive. The only real mechanism is pumped hydro schemes. Of course, we can discuss this.

Grid operators have a challenge – balance demand with supply (because storage capacity is virtually zero). Demand is variable and although there is some predictability, there are unexpected changes even in the short term.

The demand curve depends on the country. For example, the UK has peak demand in the winter evenings. Wealthy hotter countries have peak demand in the summer in the middle of the day (air-conditioning).

There are two important principles:

Grid operators already have to deal with intermittency because conventional power stations go off-line with planned outages and with unplanned, last minute, outages

Renewables have a “capacity credit” that is usually less than their expected output

The first is a simple one. An example is the Sizewell B nuclear power station in the UK supplying about 1GW [fixed] out of 80GW of total grid supply. From time to time it shuts down and the grid operator gets very little notice. So grid operators already have to deal with this. They use statistical calculations to ensure excess supply during normal operation, based on an acceptable “loss of load probability”. Total electricity demand is variable and supply is continually adjusted to match that demand. Of course, the scale of intermittency from large penetration of renewables may present challenges that are difficult to deal with by comparison with current intermittency.

The second is the difficult one. Here’s an example from a textbook by Godfrey, that’s actually a collection of articles on (mainly) UK renewables:

The essence of the calculation is a probabilistic one. At small penetration levels, the energy input from wind power displaces the need for energy generation from traditional sources. But as the percentage of wind power increases, the “potential down time” causes more problems – requiring more backup generation on standby. In the calculations above, wind going from 0.5 GW to 25 GW only saves 4 GW in conventional “capacity”. This is the meaning of capacity credit – adding 25 GW of wind power (under this simulation) provides a capacity credit of only 4 GW. So you can’t remove 25 GW of conventional from the grid, you can only remove 4 GW of conventional power.

Now the calculation of capacity credit depends on the specifics of the history of wind speeds in the region. Increasing the geographical spread of wind power generation produces better results, dependent on the lower correlation of wind speeds across larger regions. Different countries get different results.

So there’s an additional cost with wind power that someone has to pay for – which increases along with the penetration of wind power. In the immediate future this might not be a problem because perhaps the capacity already exists and is just being put on standby. However, at some stage these older plants will be at end of life and conventional plants will need to be built to provide backup.

Many calculations exist of the estimated $/MWh from providing such a backup. We will dig into those in future articles. My initial impression is that there are a lot of unknowns in the real cost of backup supply because for much potential backup supply the lifetime / maintenance impact of frequent start-stops is unclear. A lot of this is thermal shock issues – each thermal cycle costs $X.. (based on the design of the plant to handle so many thousand starts before a major overhaul is needed).

The Other Side of the Equation – Conventional Power

It will also be interesting to get some data around conventional power. Right now, the cost of displacing conventional power is new investment in renewables, but keeping conventional power is not free. Every existing station has a life and will one day need to be replaced (or demand will need to be reduced). It might be a deferred cost but it will still be a cost.

$ and GHG emissions

There is a cost to adding 1GW of wind power. There is a cost to adding 1GW of solar power. There is also a GHG cost – that is, building a solar panel or a wind turbine is not energy free and must be producing GHGs in the process. It would be interesting to get some data on this also.

Conclusion – Introduction

I wrote this article because finding real data is demanding and many websites focused on the topic are advocacy-based with minimal data. Their starting point is often the insane folly and/or mendacious intent of “the other side”. The approach we will take here is to gather and analyze data.. As if the future of the world was not at stake. As if it was not a headlong rush into lunacy to try and generate most energy from renewables.. As if it was not an unbelievable sin to continue to create electricity from fossil fuels..

This approach might allow us to form conclusions from the data rather than the reverse.

Let’s see how this approach goes.

I am hoping many current (and future) readers can contribute to the discussion – with data, uncertainties, clarifications.

I’m not expecting to be able to produce “a number” for windpower or solar power. I’m hopeful that with some research, analysis and critical questions we might be able to summarize some believable range of values for the different elements of building a renewable energy supply, and also quantify the uncertainties.

Most of what I will write in future articles I don’t yet know. Perhaps someone already has a website where this project is already complete and in my Part Two will just point readers there..

In Part Nine – Data I – Ts vs OLR we looked at the monthly surface temperature (“skin temperature”) from NCAR vs OLR measured by CERES. The slope of the data was about 2 W/m² per 1K surface temperature change. Commentators pointed out that this was really the seasonal relationship – it probably didn’t indicate anything further.

In Part Ten we looked at anomaly data: first where monthly means were removed; and then where daily means were removed. Mostly the data appeared to be a big noisy scatter plot with no slope. The main reason that I could see for this lack of relationship was that anomaly data didn’t “keep going” in one direction for more than a few days. So it’s perhaps unreasonable to expect that we would find any relationship, given that most circulation changes take time.

We haven’t yet looked at regional versions of Ts vs OLR, the main reason is I can’t yet see what we can usefully plot. A large amount of heat is exported from the tropics to the poles and so without being able to itemize the amount of heat lost from a tropical region or the amount of heat gained by a mid-latitude or polar region, what could we deduce? One solution is to look at the whole globe in totality – which is what we have done.

In this article we’ll look at the mean global annual data. We only have CERES data for complete years from 2001 to 2013 (data wasn’t available to end of the 2014 when I downloaded it).

Here are the time-series plots for surface temperature and OLR:

Figure 1

Here is the scatter plot of the above data, along with the best-fit linear interpolation:

Figure 2

The calculated slope is similar to the results we obtained from the monthly data (which probably showed the seasonal relationship). This is definitely the year to year data, but also gives us a slope that indicates positive feedback. The correlation is not strong, as indicated by the R² value of 0.37, but it exists.

As explained in previous posts, a change of 3.6 W/m² per 1K is a “no feedback” relationship, where a uniform 1K change in surface & atmospheric temperature causes an OLR increase of 3.6 W/m² due to increased surface and atmospheric radiation – a greater increase in OLR would be negative feedback and a smaller increase would be positive feedback (e.g. see Part Eight with the plot of OLR changes vs latitude and height, which integrated globally gives 3.6 W/m²).

The problem of the”no feedback” calculation is perhaps a bit more complicated and I want to dig into this calculation at some stage.

I haven’t looked at whether the result is sensitive to the date of the start of year. Next, I want to look at the changes in humidity, especially upper tropospheric water vapor, which is a key area for radiative changes. This will be a bit of work, because AIRS data comes in big files (there is a lot of data).

[I was going to post this new article not long after the last article in the series, but felt I was missing something important and needed to think about it. Instead I’ve not had any new insights and am posting for comment.]

In Part Nine – Data I, we looked at the relationship between Ts (surface temperature) and OLR (outgoing longwave radiation), for reasons all explained there.

The relationship shown there appears to be primarily the seasonal relationship, which looks like a positive feedback due to the 2W/m² per 1K temperature increase. What about the feedback on a different timescale from the seasonal relationship?

From the 2001-2013 data, here is the monthly mean and the daily mean for both Ts and OLR:

Figure 1

If we remove the monthly mean from the data, here are those same relationships (shown in the last article as anomalies from the overall 2001-2013 mean):

Figure 2 – Click to Expand

On a lag of 1 day there is a possible relationship with a low correlation – and the rest of the lags show no relationship at all.

Of course, we have created a problem with this new dataset – as the lag increases we are “jumping boundaries”. For example, on the 7-day lag all of the Ts data in the last week of April is being compared with the OLR data in the first week of May. With slowly rising temperatures, the last week of April will be “positive temperature data”, but the first week of May will be “negative OLR data”. So we expect 1/4 of our data to show the opposite relationship.

So we can show the data with the “monthly boundary jumps removed” – which means we can only show lags of say 1-14 days (with 3% – 50% of the data cut out); and we can also show the data as anomalies from the daily mean. Both have the potential to demonstrate the feedback on shorter timescales than the seasonal cycle.

First, here is the data with daily means removed:

Figure 3 – Click to Expand

Second, here is the data with the monthly means removed as in figure 2, but this time ensuring that no monthly boundaries are crossed (so some of the data is removed to ensure this):

Figure 4 – Click to Expand

So basically this demonstrates no correlation between change in daily global OLR and change in daily global temperature on less than seasonal timescales. (Or “operator error” with the creation of my anomaly data). This is excluding (because we haven’t tested it here) the very short timescale of day to night change.

This was surprising at first sight.

That is, we see global Ts increasing on a given day but we can’t distinguish any corresponding change in global OLR from random changes, at least until we get to seasonal time periods? (See graph in last article).

Then what is probably the reason came into view. Remember that this is anomaly data (daily global temperature with monthly mean subtracted). This bar graph demonstrates that when we are looking at anomaly data, most of the changes in global Ts are reversed the next day, or usually within a few days:

Figure 5

This means that we are unlikely to see changes in Ts causing noticeable changes in OLR unless the climate response we are looking for (humidity and cloud changes) occurs within a day or two.

That’s my preliminary thinking, looking at the data – i.e., we can’t expect to see much of a relationship, and we don’t see any relationship.

One further point – explained in much more detail in the (short) series Measuring Climate Sensitivity – is that of course changes in temperature are not caused by some mechanism that is independent of radiative forcing.

That is, our measurement problem is compounded by changes in temperature being first caused by fluctuations in radiative forcing (the radiation balance) and ocean heat changes and then we are measuring the “resulting” change in the radiation balance resulting from this temperature change:

Part Six – Nonlinearity and Dry Atmospheres – demonstrating that different distributions of water vapor yet with the same mean can result in different radiation to space, and how this is important for drier regions like the sub-tropics